Part A - 20 Marks

DOMAIN: Entertainment

• CONTEXT: Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene. The data labelling is already done. Since there higher time complexity is involved in the

• DATA DESCRIPTION: The dataset comprises of images and its mask for corresponding human face.

• PROJECT OBJECTIVE: To build a face detection system.

Steps and tasks: [ Total Score: 20 Marks]

1. Import and Understand the data [7 Marks]

A. Import and read ‘images.npy’. [1 Marks]

B. Split the data into Features(X) & labels(Y). Unify shape of all the images. [3 Marks]

Imp Note: Replace all the pixels within masked area with 1. Hint: X will comprise of array of image whereas Y will comprise of coordinates of the mask(human face). Observe: data[0], data[0][0], data[0][1].

C. Split the data into train and test[400:9]. [1 Marks]

D. Select random image from the train data and display original image and masked image. [2 Marks]

2. Model building [11 Marks]

A. Design a face mask detection model. [4 Marks]

Hint: 1. Use MobileNet architecture for initial pre-trained non-trainable layers. Hint: 2. Add appropriate Upsampling layers to imitate U-net architecture.

B. Design your own Dice Coefficient and Loss function. [2 Marks]

C. Train and tune the model as required. [3 Marks]

D. Evaluate and share insights on performance of the model. [2 Marks]

3. Test the model predictions on the test image: ‘image with index 3 in the test data’ and visualise the predicted masks on the faces in the image. [2 Marks]

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

Import and Understand the data [7 Marks]

A. Import and read ‘images.npy’. [1 Marks]

import numpy as np
data = np.load('/content/drive/My Drive/Colab Notebooks/Images.npy', allow_pickle=True)

B. Split the data into Features (X) & labels (Y). Unify shape of all the images. [3 Marks]

data[10][1]

[{'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.046296296296296294, 'y': 0.16354166666666667},
   {'x': 0.2037037037037037, 'y': 0.2989583333333333}],
  'imageWidth': 648,
  'imageHeight': 960},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.3194444444444444, 'y': 0.1375},
   {'x': 0.5570987654320988, 'y': 0.29583333333333334}],
  'imageWidth': 648,
  'imageHeight': 960},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.5648148148148148, 'y': 0.1875},
   {'x': 0.7145061728395061, 'y': 0.30520833333333336}],
  'imageWidth': 648,
  'imageHeight': 960},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.7160493827160493, 'y': 0.209375},
   {'x': 0.8333333333333334, 'y': 0.30625}],
  'imageWidth': 648,
  'imageHeight': 960},
 {'label': ['Face'],
  'notes': '',
  'points': [{'x': 0.8225308641975309, 'y': 0.23125},
   {'x': 0.9429012345679012, 'y': 0.3177083333333333}],
  'imageWidth': 648,
  'imageHeight': 960}]

Setting image dimensions

IMAGE_WIDTH = 224
IMAGE_HEIGHT = 224

Creating features and labels

import cv2
from tensorflow.keras.applications.mobilenet import preprocess_input

masks = np.zeros((int(data.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH))
X = np.zeros((int(data.shape[0]), IMAGE_HEIGHT, IMAGE_WIDTH, 3))
for index in range(data.shape[0]):
    img = data[index][0]
    img = cv2.resize(img, dsize=(IMAGE_HEIGHT, IMAGE_WIDTH), interpolation=cv2.INTER_CUBIC)
    try:
      img = img[:, :, :3]
    except:
      continue
    X[index] = preprocess_input(np.array(img, dtype=np.float32))
    for i in data[index][1]:
        x1 = int(i["points"][0]['x'] * IMAGE_WIDTH)
        x2 = int(i["points"][1]['x'] * IMAGE_WIDTH)
        y1 = int(i["points"][0]['y'] * IMAGE_HEIGHT)
        y2 = int(i["points"][1]['y'] * IMAGE_HEIGHT)
        masks[index][y1:y2, x1:x2] = 1

C. Split the data into train and test[400:9]. [1 Marks]

400 images in training

9 images in testing data

Training data

X_train = X[:400]
y_train = masks[:400]

Testing data

X_test = X[400:]
y_test = masks[400:]

D. Select random image from the train data and display original image and masked image. [2 Marks]

import matplotlib.pyplot as plt

n = 10
print("Image array:", X_train[n])
plt.imshow(X_train[n])

WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

Image array: [[[-0.95294118 -0.67058825 -0.56078434]
  [-0.95294118 -0.67058825 -0.56078434]
  [-0.95294118 -0.67058825 -0.56078434]
  ...
  [-0.92941177 -0.63137257 -0.52941179]
  [-0.92941177 -0.63137257 -0.52941179]
  [-0.92941177 -0.63137257 -0.52941179]]

 [[-0.95294118 -0.67058825 -0.56078434]
  [-0.95294118 -0.67058825 -0.56078434]
  [-0.95294118 -0.67058825 -0.56078434]
  ...
  [-0.92941177 -0.63137257 -0.52941179]
  [-0.92941177 -0.63137257 -0.52941179]
  [-0.92941177 -0.63137257 -0.52941179]]

 [[-0.95294118 -0.67058825 -0.56078434]
  [-0.95294118 -0.67058825 -0.56078434]
  [-0.95294118 -0.67058825 -0.56078434]
  ...
  [-0.92941177 -0.63137257 -0.52941179]
  [-0.92941177 -0.63137257 -0.52941179]
  [-0.92941177 -0.63137257 -0.52941179]]

 ...

 [[-0.99215686 -0.99215686 -0.99215686]
  [-0.99215686 -0.99215686 -0.99215686]
  [-0.99215686 -0.99215686 -0.99215686]
  ...
  [-0.99215686 -0.99215686 -0.99215686]
  [-0.99215686 -0.99215686 -0.99215686]
  [-0.99215686 -0.99215686 -0.99215686]]

 [[-0.95294118 -0.95294118 -0.95294118]
  [-1.         -1.         -1.        ]
  [-0.372549   -0.372549   -0.372549  ]
  ...
  [-0.36470586 -0.36470586 -0.36470586]
  [-1.         -1.         -1.        ]
  [-1.         -1.         -1.        ]]

 [[-0.99215686 -0.99215686 -0.99215686]
  [-0.97647059 -0.97647059 -0.97647059]
  [-0.94509804 -0.94509804 -0.94509804]
  ...
  [-0.95294118 -0.95294118 -0.95294118]
  [-1.         -1.         -1.        ]
  [-1.         -1.         -1.        ]]]

<matplotlib.image.AxesImage at 0x78e2eff0db70>

plt.imshow(masks[n])

<matplotlib.image.AxesImage at 0x7aece4aa24d0>

2. Model building [11 Marks]

A. Design a face mask detection model. [4 Marks]

Hint: 1. Use MobileNet architecture for initial pre-trained non-trainable layers.

Hint: 2. Add appropriate Upsampling layers to imitate U-net architecture.

Addding MobileNet as model with below parameter values

input_shape: IMAGE_HEIGHT, IMAGE_WIDTH, 3

include_top: False

alpha: 1.0

weights: "imagenet"

Adding UNET architecture layers

from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.layers import Reshape, UpSampling2D, Concatenate, Conv2D
from tensorflow.keras.models import Model

def create_model(trainable=True):
    model = MobileNet(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), include_top=False, alpha=1.0, weights="imagenet")

    for layer in model.layers:
        layer.trainable = trainable

    block0 = model.get_layer("conv_pw_1_relu").output
    block1 = model.get_layer("conv_pw_3_relu").output
    block2 = model.get_layer("conv_pw_5_relu").output
    block3 = model.get_layer("conv_pw_11_relu").output
    block4 = model.get_layer("conv_pw_13_relu").output

    x = Concatenate()([UpSampling2D()(block4), block3])
    print(x.shape)
    x = Concatenate()([UpSampling2D()(x), block2])
    print(x.shape)
    x = Concatenate()([UpSampling2D()(x), block1])
    print(x.shape)
    x = Concatenate()([UpSampling2D()(x), block0])
    print(x.shape)
    x = UpSampling2D()(x)
    print(x.shape)
    x = Conv2D(1, kernel_size=1, activation="sigmoid")(x)
    x = Reshape((IMAGE_HEIGHT, IMAGE_WIDTH))(x)
    print(x.shape)

    return Model(inputs=model.input, outputs=x)

# Calling the create_model function

model = create_model()

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5
17225924/17225924 [==============================] - 0s 0us/step
(None, 14, 14, 1536)
(None, 28, 28, 1792)
(None, 56, 56, 1920)
(None, 112, 112, 1984)
(None, 224, 224, 1984)
(None, 224, 224)

# Printing model summary
model.summary()

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                Output Shape                 Param #   Connected to                  
==================================================================================================
 input_1 (InputLayer)        [(None, 224, 224, 3)]        0         []                            
                                                                                                  
 conv1 (Conv2D)              (None, 112, 112, 32)         864       ['input_1[0][0]']             
                                                                                                  
 conv1_bn (BatchNormalizati  (None, 112, 112, 32)         128       ['conv1[0][0]']               
 on)                                                                                              
                                                                                                  
 conv1_relu (ReLU)           (None, 112, 112, 32)         0         ['conv1_bn[0][0]']            
                                                                                                  
 conv_dw_1 (DepthwiseConv2D  (None, 112, 112, 32)         288       ['conv1_relu[0][0]']          
 )                                                                                                
                                                                                                  
 conv_dw_1_bn (BatchNormali  (None, 112, 112, 32)         128       ['conv_dw_1[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_1_relu (ReLU)       (None, 112, 112, 32)         0         ['conv_dw_1_bn[0][0]']        
                                                                                                  
 conv_pw_1 (Conv2D)          (None, 112, 112, 64)         2048      ['conv_dw_1_relu[0][0]']      
                                                                                                  
 conv_pw_1_bn (BatchNormali  (None, 112, 112, 64)         256       ['conv_pw_1[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_1_relu (ReLU)       (None, 112, 112, 64)         0         ['conv_pw_1_bn[0][0]']        
                                                                                                  
 conv_pad_2 (ZeroPadding2D)  (None, 113, 113, 64)         0         ['conv_pw_1_relu[0][0]']      
                                                                                                  
 conv_dw_2 (DepthwiseConv2D  (None, 56, 56, 64)           576       ['conv_pad_2[0][0]']          
 )                                                                                                
                                                                                                  
 conv_dw_2_bn (BatchNormali  (None, 56, 56, 64)           256       ['conv_dw_2[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_2_relu (ReLU)       (None, 56, 56, 64)           0         ['conv_dw_2_bn[0][0]']        
                                                                                                  
 conv_pw_2 (Conv2D)          (None, 56, 56, 128)          8192      ['conv_dw_2_relu[0][0]']      
                                                                                                  
 conv_pw_2_bn (BatchNormali  (None, 56, 56, 128)          512       ['conv_pw_2[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_2_relu (ReLU)       (None, 56, 56, 128)          0         ['conv_pw_2_bn[0][0]']        
                                                                                                  
 conv_dw_3 (DepthwiseConv2D  (None, 56, 56, 128)          1152      ['conv_pw_2_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_3_bn (BatchNormali  (None, 56, 56, 128)          512       ['conv_dw_3[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_3_relu (ReLU)       (None, 56, 56, 128)          0         ['conv_dw_3_bn[0][0]']        
                                                                                                  
 conv_pw_3 (Conv2D)          (None, 56, 56, 128)          16384     ['conv_dw_3_relu[0][0]']      
                                                                                                  
 conv_pw_3_bn (BatchNormali  (None, 56, 56, 128)          512       ['conv_pw_3[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_3_relu (ReLU)       (None, 56, 56, 128)          0         ['conv_pw_3_bn[0][0]']        
                                                                                                  
 conv_pad_4 (ZeroPadding2D)  (None, 57, 57, 128)          0         ['conv_pw_3_relu[0][0]']      
                                                                                                  
 conv_dw_4 (DepthwiseConv2D  (None, 28, 28, 128)          1152      ['conv_pad_4[0][0]']          
 )                                                                                                
                                                                                                  
 conv_dw_4_bn (BatchNormali  (None, 28, 28, 128)          512       ['conv_dw_4[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_4_relu (ReLU)       (None, 28, 28, 128)          0         ['conv_dw_4_bn[0][0]']        
                                                                                                  
 conv_pw_4 (Conv2D)          (None, 28, 28, 256)          32768     ['conv_dw_4_relu[0][0]']      
                                                                                                  
 conv_pw_4_bn (BatchNormali  (None, 28, 28, 256)          1024      ['conv_pw_4[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_4_relu (ReLU)       (None, 28, 28, 256)          0         ['conv_pw_4_bn[0][0]']        
                                                                                                  
 conv_dw_5 (DepthwiseConv2D  (None, 28, 28, 256)          2304      ['conv_pw_4_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_5_bn (BatchNormali  (None, 28, 28, 256)          1024      ['conv_dw_5[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_5_relu (ReLU)       (None, 28, 28, 256)          0         ['conv_dw_5_bn[0][0]']        
                                                                                                  
 conv_pw_5 (Conv2D)          (None, 28, 28, 256)          65536     ['conv_dw_5_relu[0][0]']      
                                                                                                  
 conv_pw_5_bn (BatchNormali  (None, 28, 28, 256)          1024      ['conv_pw_5[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_5_relu (ReLU)       (None, 28, 28, 256)          0         ['conv_pw_5_bn[0][0]']        
                                                                                                  
 conv_pad_6 (ZeroPadding2D)  (None, 29, 29, 256)          0         ['conv_pw_5_relu[0][0]']      
                                                                                                  
 conv_dw_6 (DepthwiseConv2D  (None, 14, 14, 256)          2304      ['conv_pad_6[0][0]']          
 )                                                                                                
                                                                                                  
 conv_dw_6_bn (BatchNormali  (None, 14, 14, 256)          1024      ['conv_dw_6[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_6_relu (ReLU)       (None, 14, 14, 256)          0         ['conv_dw_6_bn[0][0]']        
                                                                                                  
 conv_pw_6 (Conv2D)          (None, 14, 14, 512)          131072    ['conv_dw_6_relu[0][0]']      
                                                                                                  
 conv_pw_6_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_pw_6[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_6_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_pw_6_bn[0][0]']        
                                                                                                  
 conv_dw_7 (DepthwiseConv2D  (None, 14, 14, 512)          4608      ['conv_pw_6_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_7_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_dw_7[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_7_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_dw_7_bn[0][0]']        
                                                                                                  
 conv_pw_7 (Conv2D)          (None, 14, 14, 512)          262144    ['conv_dw_7_relu[0][0]']      
                                                                                                  
 conv_pw_7_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_pw_7[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_7_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_pw_7_bn[0][0]']        
                                                                                                  
 conv_dw_8 (DepthwiseConv2D  (None, 14, 14, 512)          4608      ['conv_pw_7_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_8_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_dw_8[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_8_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_dw_8_bn[0][0]']        
                                                                                                  
 conv_pw_8 (Conv2D)          (None, 14, 14, 512)          262144    ['conv_dw_8_relu[0][0]']      
                                                                                                  
 conv_pw_8_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_pw_8[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_8_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_pw_8_bn[0][0]']        
                                                                                                  
 conv_dw_9 (DepthwiseConv2D  (None, 14, 14, 512)          4608      ['conv_pw_8_relu[0][0]']      
 )                                                                                                
                                                                                                  
 conv_dw_9_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_dw_9[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_dw_9_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_dw_9_bn[0][0]']        
                                                                                                  
 conv_pw_9 (Conv2D)          (None, 14, 14, 512)          262144    ['conv_dw_9_relu[0][0]']      
                                                                                                  
 conv_pw_9_bn (BatchNormali  (None, 14, 14, 512)          2048      ['conv_pw_9[0][0]']           
 zation)                                                                                          
                                                                                                  
 conv_pw_9_relu (ReLU)       (None, 14, 14, 512)          0         ['conv_pw_9_bn[0][0]']        
                                                                                                  
 conv_dw_10 (DepthwiseConv2  (None, 14, 14, 512)          4608      ['conv_pw_9_relu[0][0]']      
 D)                                                                                               
                                                                                                  
 conv_dw_10_bn (BatchNormal  (None, 14, 14, 512)          2048      ['conv_dw_10[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_dw_10_relu (ReLU)      (None, 14, 14, 512)          0         ['conv_dw_10_bn[0][0]']       
                                                                                                  
 conv_pw_10 (Conv2D)         (None, 14, 14, 512)          262144    ['conv_dw_10_relu[0][0]']     
                                                                                                  
 conv_pw_10_bn (BatchNormal  (None, 14, 14, 512)          2048      ['conv_pw_10[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_pw_10_relu (ReLU)      (None, 14, 14, 512)          0         ['conv_pw_10_bn[0][0]']       
                                                                                                  
 conv_dw_11 (DepthwiseConv2  (None, 14, 14, 512)          4608      ['conv_pw_10_relu[0][0]']     
 D)                                                                                               
                                                                                                  
 conv_dw_11_bn (BatchNormal  (None, 14, 14, 512)          2048      ['conv_dw_11[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_dw_11_relu (ReLU)      (None, 14, 14, 512)          0         ['conv_dw_11_bn[0][0]']       
                                                                                                  
 conv_pw_11 (Conv2D)         (None, 14, 14, 512)          262144    ['conv_dw_11_relu[0][0]']     
                                                                                                  
 conv_pw_11_bn (BatchNormal  (None, 14, 14, 512)          2048      ['conv_pw_11[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_pw_11_relu (ReLU)      (None, 14, 14, 512)          0         ['conv_pw_11_bn[0][0]']       
                                                                                                  
 conv_pad_12 (ZeroPadding2D  (None, 15, 15, 512)          0         ['conv_pw_11_relu[0][0]']     
 )                                                                                                
                                                                                                  
 conv_dw_12 (DepthwiseConv2  (None, 7, 7, 512)            4608      ['conv_pad_12[0][0]']         
 D)                                                                                               
                                                                                                  
 conv_dw_12_bn (BatchNormal  (None, 7, 7, 512)            2048      ['conv_dw_12[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_dw_12_relu (ReLU)      (None, 7, 7, 512)            0         ['conv_dw_12_bn[0][0]']       
                                                                                                  
 conv_pw_12 (Conv2D)         (None, 7, 7, 1024)           524288    ['conv_dw_12_relu[0][0]']     
                                                                                                  
 conv_pw_12_bn (BatchNormal  (None, 7, 7, 1024)           4096      ['conv_pw_12[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_pw_12_relu (ReLU)      (None, 7, 7, 1024)           0         ['conv_pw_12_bn[0][0]']       
                                                                                                  
 conv_dw_13 (DepthwiseConv2  (None, 7, 7, 1024)           9216      ['conv_pw_12_relu[0][0]']     
 D)                                                                                               
                                                                                                  
 conv_dw_13_bn (BatchNormal  (None, 7, 7, 1024)           4096      ['conv_dw_13[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_dw_13_relu (ReLU)      (None, 7, 7, 1024)           0         ['conv_dw_13_bn[0][0]']       
                                                                                                  
 conv_pw_13 (Conv2D)         (None, 7, 7, 1024)           1048576   ['conv_dw_13_relu[0][0]']     
                                                                                                  
 conv_pw_13_bn (BatchNormal  (None, 7, 7, 1024)           4096      ['conv_pw_13[0][0]']          
 ization)                                                                                         
                                                                                                  
 conv_pw_13_relu (ReLU)      (None, 7, 7, 1024)           0         ['conv_pw_13_bn[0][0]']       
                                                                                                  
 up_sampling2d (UpSampling2  (None, 14, 14, 1024)         0         ['conv_pw_13_relu[0][0]']     
 D)                                                                                               
                                                                                                  
 concatenate (Concatenate)   (None, 14, 14, 1536)         0         ['up_sampling2d[0][0]',       
                                                                     'conv_pw_11_relu[0][0]']     
                                                                                                  
 up_sampling2d_1 (UpSamplin  (None, 28, 28, 1536)         0         ['concatenate[0][0]']         
 g2D)                                                                                             
                                                                                                  
 concatenate_1 (Concatenate  (None, 28, 28, 1792)         0         ['up_sampling2d_1[0][0]',     
 )                                                                   'conv_pw_5_relu[0][0]']      
                                                                                                  
 up_sampling2d_2 (UpSamplin  (None, 56, 56, 1792)         0         ['concatenate_1[0][0]']       
 g2D)                                                                                             
                                                                                                  
 concatenate_2 (Concatenate  (None, 56, 56, 1920)         0         ['up_sampling2d_2[0][0]',     
 )                                                                   'conv_pw_3_relu[0][0]']      
                                                                                                  
 up_sampling2d_3 (UpSamplin  (None, 112, 112, 1920)       0         ['concatenate_2[0][0]']       
 g2D)                                                                                             
                                                                                                  
 concatenate_3 (Concatenate  (None, 112, 112, 1984)       0         ['up_sampling2d_3[0][0]',     
 )                                                                   'conv_pw_1_relu[0][0]']      
                                                                                                  
 up_sampling2d_4 (UpSamplin  (None, 224, 224, 1984)       0         ['concatenate_3[0][0]']       
 g2D)                                                                                             
                                                                                                  
 conv2d (Conv2D)             (None, 224, 224, 1)          1985      ['up_sampling2d_4[0][0]']     
                                                                                                  
 reshape (Reshape)           (None, 224, 224)             0         ['conv2d[0][0]']              
                                                                                                  
==================================================================================================
Total params: 3230849 (12.32 MB)
Trainable params: 3208961 (12.24 MB)
Non-trainable params: 21888 (85.50 KB)
__________________________________________________________________________________________________

B. Design your own Dice Coefficient and Loss function. [2 Marks]

# Creating a function to calculate dice coefficient

from tensorflow import reduce_sum
from tensorflow.keras.backend import epsilon

def dice_coefficient(y_true, y_pred):
    numerator = 2 * reduce_sum(y_true * y_pred)
    denominator = reduce_sum(y_true + y_pred)
    return numerator / (denominator + epsilon())

# Defining loss function

from tensorflow.keras.losses import binary_crossentropy
from tensorflow.keras.backend import log

def loss(y_true, y_pred):
    return binary_crossentropy(y_true, y_pred) - log(dice_coefficient(y_true, y_pred) + epsilon())

C. Train and tune the model as required. [3 Marks]

# Compiling the model using below parameters
# loss: using the loss function defined above
# optimizer: using Adam optimizer
# metrics: using dice_coefficient function defined above

model.compile(loss=loss, optimizer='adam', metrics=[dice_coefficient])

# Defining callbacks
# Using ModelCheckpoint
# Using EarlyStopping
# Using ReduceLROnPlateau

from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau

checkpoint = ModelCheckpoint("model-{loss:.2f}.h5", monitor="loss", verbose=1, save_best_only=True,
                             save_weights_only=True, mode="min")
stop = EarlyStopping(monitor="loss", patience=5, mode="min")
reduce_lr = ReduceLROnPlateau(monitor="loss", factor=0.2, patience=5, min_lr=1e-6, verbose=1, mode="min")

# Fitting the model using below parameters
# epochs: 10
# batch_size: 1
# callbacks: using the callbacks defined above

model.fit(X_train, y_train, epochs=10, batch_size=1, callbacks=[checkpoint, reduce_lr, stop])

Epoch 1/10
393/393 [==============================] - ETA: 0s - loss: 1.2121 - dice_coefficient: 0.5100
Epoch 1: loss improved from inf to 1.21206, saving model to model-1.21.h5
393/393 [==============================] - 751s 2s/step - loss: 1.2121 - dice_coefficient: 0.5100 - lr: 0.0010
Epoch 2/10
393/393 [==============================] - ETA: 0s - loss: 0.8327 - dice_coefficient: 0.6094
Epoch 2: loss improved from 1.21206 to 0.83273, saving model to model-0.83.h5
393/393 [==============================] - 689s 2s/step - loss: 0.8327 - dice_coefficient: 0.6094 - lr: 0.0010
Epoch 3/10
393/393 [==============================] - ETA: 0s - loss: 0.6995 - dice_coefficient: 0.6541
Epoch 3: loss improved from 0.83273 to 0.69950, saving model to model-0.70.h5
393/393 [==============================] - 692s 2s/step - loss: 0.6995 - dice_coefficient: 0.6541 - lr: 0.0010
Epoch 4/10
393/393 [==============================] - ETA: 0s - loss: 0.6060 - dice_coefficient: 0.6891
Epoch 4: loss improved from 0.69950 to 0.60599, saving model to model-0.61.h5
393/393 [==============================] - 699s 2s/step - loss: 0.6060 - dice_coefficient: 0.6891 - lr: 0.0010
Epoch 5/10
393/393 [==============================] - ETA: 0s - loss: 0.5717 - dice_coefficient: 0.7062
Epoch 5: loss improved from 0.60599 to 0.57166, saving model to model-0.57.h5
393/393 [==============================] - 690s 2s/step - loss: 0.5717 - dice_coefficient: 0.7062 - lr: 0.0010
Epoch 6/10
393/393 [==============================] - ETA: 0s - loss: 0.5148 - dice_coefficient: 0.7319
Epoch 6: loss improved from 0.57166 to 0.51481, saving model to model-0.51.h5
393/393 [==============================] - 711s 2s/step - loss: 0.5148 - dice_coefficient: 0.7319 - lr: 0.0010
Epoch 7/10
393/393 [==============================] - ETA: 0s - loss: 0.4755 - dice_coefficient: 0.7502
Epoch 7: loss improved from 0.51481 to 0.47551, saving model to model-0.48.h5
393/393 [==============================] - 731s 2s/step - loss: 0.4755 - dice_coefficient: 0.7502 - lr: 0.0010
Epoch 8/10
393/393 [==============================] - ETA: 0s - loss: 0.4324 - dice_coefficient: 0.7720
Epoch 8: loss improved from 0.47551 to 0.43244, saving model to model-0.43.h5
393/393 [==============================] - 737s 2s/step - loss: 0.4324 - dice_coefficient: 0.7720 - lr: 0.0010
Epoch 9/10
393/393 [==============================] - ETA: 0s - loss: 0.4556 - dice_coefficient: 0.7634
Epoch 9: loss did not improve from 0.43244
393/393 [==============================] - 722s 2s/step - loss: 0.4556 - dice_coefficient: 0.7634 - lr: 0.0010
Epoch 10/10
393/393 [==============================] - ETA: 0s - loss: 0.5264 - dice_coefficient: 0.7333
Epoch 10: loss did not improve from 0.43244
393/393 [==============================] - 713s 2s/step - loss: 0.5264 - dice_coefficient: 0.7333 - lr: 0.0010

<keras.src.callbacks.History at 0x7aece410fd00>

D. Evaluate and share insights on performance of the model. [2 Marks]

From the training logs, we can derive several insights into the performance and behavior of the model over the 10 epochs of training:

1. Initial Improvement:

   • The model showed significant improvement in the initial epochs. The loss decreased from 1.2121 to 0.8327 from Epoch 1 to Epoch 2, indicating that the model was learning and the weights were being updated effectively.

2. Steady Progress:

   • The loss continued to improve consistently until Epoch 8, reaching a minimum of 0.4324. This steady decrease in loss suggests that the model was effectively learning and generalizing well during these epochs.
   • Correspondingly, the Dice coefficient, which measures the similarity between the predicted and actual outputs, improved from 0.5100 in Epoch 1 to 0.7720 in Epoch 8. This increase indicates that the model's predictions became more accurate over time.

3. Plateauing Performance:

   • After Epoch 8, the performance plateaued. The loss increased slightly in Epochs 9 and 10 to 0.4556 and 0.5264, respectively, and the Dice coefficient decreased slightly to 0.7634 and 0.7333. This suggests that the model may have reached its learning capacity given the current architecture and hyperparameters.

4. Learning Rate:

   • The learning rate was constant at 0.001 throughout the training. Given the plateau observed, it might be beneficial to implement a learning rate scheduler that reduces the learning rate after a certain number of epochs without improvement.

5. Model Saving:

   • The model was saved at the end of each epoch if the loss improved. The filenames indicate the loss value at which the model was saved. This is a good practice to ensure that the best performing model is preserved.

6. Potential Overfitting:

   • The increase in loss and slight decrease in the Dice coefficient in the last two epochs could indicate the beginning of overfitting, where the model starts to perform worse on the validation data than on the training data. To confirm this, it would be necessary to compare the training and validation losses and metrics.

Overall, the model showed good initial learning and steady progress but began to overfit in the later stages. By implementing some improvements, it may be possible to achieve better and more consistent performance.

3. Test the model predictions on the test image: ‘image with index 3 in the test data’ and visualise the predicted masks on the faces in the image. [2 Marks]

# Getting the predicted mask for a test image
# Showing the test image
import matplotlib.pyplot as plt
m = 3
plt.imshow(X_test[m])

<matplotlib.image.AxesImage at 0x78e243ffb130>

# Show original mask for test image
plt.imshow(masks[m])

<matplotlib.image.AxesImage at 0x78e248163b20>

# Predicting the mask on the test image
import cv2
import numpy as np
import matplotlib.pyplot as plt



# Predict the mask on the resized test image
pred_mask = model.predict(np.array([resized_image]))[0] > 0.5

# Optionally, resize the predicted mask back to the original image size for visualization
original_shape = X_test[m].shape[:2]
pred_mask_resized = cv2.resize(pred_mask.astype(np.float32), original_shape)

# Plot the original image and the predicted mask
plt.figure(figsize=(10, 5))

plt.subplot(1, 2, 1)
plt.title('Original Image')
plt.imshow(X_test[m])

plt.subplot(1, 2, 2)
plt.title('Predicted Mask')
plt.imshow(pred_mask_resized, cmap='gray')

plt.show()

1/1 [==============================] - 1s 894ms/step

# Imposing the mask on the test image
# In imshow using the alpha parameter and setting it to 0.9 (i.e. > 0.5)
plt.imshow(X_test[m])
plt.imshow(pred_mask, alpha=0.9)

<matplotlib.image.AxesImage at 0x78e258305e10>

Part B - 10 Marks

• DOMAIN: Entertainment

• CONTEXT:

Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene. The data labelling is already done. Since there higher time complexity is involved in the

• DATA DESCRIPTION: The dataset comprises of face images.

• PROJECT OBJECTIVE: To create an image dataset to be used by AI team build an image classifier data. Profile images of people are given.

Steps and tasks: [ Total Score: 10 Marks]

1. Read/import images from folder ‘training_images’. [2 Marks]
2. Write a loop which will iterate through all the images in the ‘training_images’ folder and detect the faces present on all the images. [3 Marks]

Hint: You can use ’haarcascade_frontalface_default.xml’ from internet to detect faces which is available open source. 3. From the same loop above, extract metadata of the faces and write into a DataFrame. [3 Marks]

4. Save the output Dataframe in .csv format. [2 Marks]

1.Read/import images from folder ‘training_images’. [2 Marks]

import os,cv2,keras
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

2: Write a loop which will iterate through all the images in the ‘training_images’ folder and detect the faces present on all the images. [3 Marks]

Hint: You can use ’haarcascade_frontalface_default.xml’ from internet to detect faces which is available open source.

# Open cv version 2
import cv2
import os
import glob
import numpy as np

img_dir = '/content/drive/My Drive/Colab Notebooks/training_images'

data_path = os.path.join(img_dir,'*g') # '*' is used to extract all files in the directory
files = glob.glob(data_path)
data = [0]  # EMPTY LIST
for f1 in files:
    img = cv2.imread(f1)
    data.append(img)

3 From the same loop above, extract metadata of the faces and write into a DataFrame. [3 Marks]

data[600]

ndarray (600, 600, 3) show data

array([[[ 90, 102, 120],
        [ 90, 102, 120],
        [ 90, 102, 120],
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       [[ 90, 102, 120],
        [ 90, 102, 120],
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        [ 67,  79,  97],
        [ 67,  79,  97]],

       [[ 90, 102, 120],
        [ 90, 102, 120],
        [ 90, 102, 120],
        ...,
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        [ 67,  79,  97],
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       ...,

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        [ 44,  90,  68],
        ...,
        [ 50,  96,  74],
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        [ 51,  97,  75]]], dtype=uint8)

dir_list = next(os.walk(img_dir))[2]
dir_list

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4 Save the output Dataframe in .csv format. [2 Marks]

import cv2
import os
import glob
import pandas as pd

# Directory containing the images
img_dir = '/content/drive/My Drive/Colab Notebooks/training_images'

# Pattern to match all image files
data_path = os.path.join(img_dir, '*g')
files = glob.glob(data_path)

# Load the Haar cascade classifier for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# List to store metadata of faces
face_metadata = []

# Loop through all the image files and detect faces
for f1 in files:
    # Read the image
    img = cv2.imread(f1)
    if img is None:
        continue

    # Convert the image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Detect faces in the image
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Extract metadata and store in the list
    for (x, y, w, h) in faces:
        face_metadata.append({
            'image_file': os.path.basename(f1),
            'x': x,
            'y': y,
            'width': w,
            'height': h
        })

# Convert the list to a DataFrame
face_metadata_df = pd.DataFrame(face_metadata)

# Save the output DataFrame in .csv format
output_csv_path = '/content/drive/My Drive/Colab Notebooks/face_metadata.csv'
face_metadata_df.to_csv(output_csv_path, index=False)

print(f"Face metadata has been saved to {output_csv_path}")

Face metadata has been saved to /content/drive/My Drive/Colab Notebooks/face_metadata.csv

Part C - 30 Marks

DOMAIN: Face Recognition

• CONTEXT: Company X intends to build a face identification model to recognise human faces.

• DATA DESCRIPTION: The dataset comprises of images and its mask where there is a human face.

• PROJECT OBJECTIVE: Face Aligned Face Dataset from Pinterest. This dataset contains 10,770 images for 100 people. All images are taken from 'Pinterest' and aligned using dlib library. Some data samples:

Steps and tasks: [ Total Score: 30 Marks]

1. Unzip, read and Load data(‘PINS.zip’) into session. [2 Marks]
2. Write function to create metadata of the image. [4 Marks]

Hint: Metadata means derived information from the available data which can be useful for particular problem statement. 3. Write a loop to iterate through each and every image and create metadata for all the images. [4 Marks] 4. Generate Embeddings vectors on the each face in the dataset. [4 Marks] Hint: Use ‘vgg_face_weights.h5’ 5. Build distance metrics for identifying the distance between two similar and dissimilar images. [4 Marks] 6. Use PCA for dimensionality reduction. [2 Marks] 7. Build an SVM classifier in order to map each image to its right person. [4 Marks] 8. Import and display the the test images. [2 Marks] Hint: ‘Benedict Cumberbatch9.jpg’ and ‘Dwayne Johnson4.jpg’ are the test images. 9. Use the trained SVM model to predict the face on both test images. [4 Marks]

1 Unzip, read and Load data(‘PINS.zip’) into session. [2 Marks]

# Mounting Google Drive
from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).

# Setting the current working directory
import os; os.chdir('/content/drive/My Drive/Colab Notebooks')

# Import Packages

# Imports
import pandas as pd, numpy as np, matplotlib.pyplot as plt, sklearn, re, random
import matplotlib.gridspec as gridspec
from tqdm.notebook import tqdm
import tensorflow, cv2
%matplotlib inline

# Extract content from zipfile
from zipfile import ZipFile

# Model
from tensorflow.keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout, Flatten, Activation
from tensorflow.keras.models import Sequential, Model
from sklearn.svm import SVC

# Encode, standardize and PCA
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.decomposition import PCA

# Set random_state
random_state = 2020

# Suppress warnings, if any
import warnings; warnings.filterwarnings('ignore')

# Print versions
print(f'Pandas version: {pd.__version__}')
print(f'Numpy version: {np.__version__}')
print(f'Scikit-learn version: {sklearn.__version__}')
print(f'Tensorflow version: {tensorflow.__version__}')
print(f'CV version: {cv2.__version__}')

Pandas version: 2.0.3
Numpy version: 1.25.2
Scikit-learn version: 1.2.2
Tensorflow version: 2.15.0
CV version: 4.8.0

# Extract the zip file
!ls

 adult_data.csv				 PINS
'Applied Statistics Project.ipynb'	 PINS.zip
'Benedict Cumberbatch9.jpg'		 practice_exercise_answers.ipynb
 ComputerVisionProject.ipynb		 practice_exercise_questions.ipynb
 CV_Week4_Case_Study_new_updated.ipynb	'practice_exercise_solutions .ipynb'
'Dwayne Johnson4.jpg'			 practice_notebook.ipynb
 face_metadata.csv			 Predicting_fleet_failure_for_Zoom_cars.ipynb
 Images.npy				 Prediction+-+Flower.jpg
 IntrotoColab.ipynb			'Python for Data Science - Practice Quiz - Week 3.ipynb'
 machinefailure.csv			 training_images
 __MACOSX				 training_images-20211126T092819Z-001.zip
'Monkeys dataset.zip'			 Untitled
 Notebook_Week1.ipynb			 vgg_face_weights.h5

with ZipFile('PINS.zip', 'r') as zip:
  zip.extractall()

2. Write function to create metadata of the image. [4 Marks]

Hint: Metadata means derived information from the available data which can be useful for particular problem statement.

# Function to load images

# Define a function to load the images from the extracted folder and map each image with person id

class IdentityMetadata():
    def __init__(self, base, name, file):
        # print(base, name, file)
        # dataset base directory
        self.base = base
        # identity name
        self.name = name
        # image file name
        self.file = file

    def __repr__(self):
        return self.image_path()

    def image_path(self):
        return os.path.join(self.base, self.name, self.file)

def load_metadata(path):
    metadata = []
    exts = []
    for i in os.listdir(path):
        for f in os.listdir(os.path.join(path, i)):
            # Check file extension. Allow only jpg/jpeg' files.
            ext = os.path.splitext(f)[1]
            if ext == '.jpg' or ext == '.jpeg':
                metadata.append(IdentityMetadata(path, i, f))
                exts.append(ext)
    return np.array(metadata), exts

metadata, exts = load_metadata('PINS')
labels = np.array([meta.name for meta in metadata])

3. Write a loop to iterate through each and every image and create metadata for all the images. [4 Marks]

# Define function to load image
# Define a function to load image from the metadata

def load_image(path):
    img = cv2.imread(path, 1)
    # OpenCV loads images with color channels
    # in BGR order. So we need to reverse them
    return img[...,::-1]

# Load a sample image
# Load one image using the function "load_image"

n = np.random.randint(1, len(metadata))
img_path = metadata[n].image_path()
img = load_image(img_path)

fig = plt.figure(figsize = (15, 7.2))
ax = fig.add_subplot(1, 1, 1)
title = labels[n].split('_')[1]
ax.set_title(title, fontsize = 20)
_ = plt.imshow(img)

4. Generate Embeddings vectors on the each face in the dataset. [4 Marks]

Hint: Use ‘vgg_face_weights.h5

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import ZeroPadding2D, Convolution2D, MaxPooling2D, Dropout, Flatten, Activation

def vgg_face():
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(224,224, 3)))
    model.add(Convolution2D(64, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Convolution2D(4096, (7, 7), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Convolution2D(4096, (1, 1), activation='relu'))
    model.add(Dropout(0.5))
    model.add(Convolution2D(2622, (1, 1)))
    model.add(Flatten())
    model.add(Activation('softmax'))
    return model

# Loading the the model defined above
# Then loading the given weight file

from tensorflow.keras.models import model_from_json
model = vgg_face()
model.load_weights('vgg_face_weights.h5')

# Get vgg_face_descriptor
from tensorflow.keras.models import Model
vgg_face_descriptor = Model(inputs=model.layers[0].input, outputs=model.layers[-2].output)

# Generating embeddings for each image in the dataset

# Getting embedding vector for first image in the metadata using the pre-trained model
img_path = metadata[0].image_path()
img = load_image(img_path)

# Normalising pixel values from [0-255] to [0-1]: scale RGB values to interval [0,1]
img = (img / 255.).astype(np.float32)

img = cv2.resize(img, dsize = (224,224))
print(img.shape)

# Obtaining embedding vector for an image
# Getting the embedding vector for the above image using vgg_face_descriptor model and printing the shape

embedding_vector = vgg_face_descriptor.predict(np.expand_dims(img, axis=0))[0]
print(embedding_vector.shape)

(224, 224, 3)
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(2622,)

Generating embeddings for all images

Writing code to iterate through metadata and create embeddings for each image using vgg_face_descriptor.predict() and storing in a list with name embeddings

If there is any error in reading any image in the dataset, we will fill the emebdding vector of that image with 2622-zeroes as the final embedding from the model is of length 2622.

import numpy as np
import cv2
from tqdm import tqdm

# Assuming the load_image function and vgg_face_descriptor are defined elsewhere

embeddings = np.zeros((metadata.shape[0], 2622))

for i, meta in tqdm(enumerate(metadata)):
  try:
    image = load_image(str(meta))
    image = (image / 255.).astype(np.float32)
    image = cv2.resize(image, (224, 224))
    embeddings[i] = vgg_face_descriptor.predict(np.expand_dims(image, axis=0))[0]
  except Exception as e:
    print(f"Error processing image {meta}: {e}")
    embeddings[i] = np.zeros(2622)

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5. Build distance metrics for identifying the distance between two similar and dissimilar images. [4 Marks]

def distance(emb1, emb2):
    return np.sum(np.square(emb1 - emb2))

# Plotting images and getting distance between the pairs given below
# 2, 3 and 2, 180
# 30, 31 and 30, 100
# 70, 72 and 70, 115

def show_pair(idx1, idx2):
    plt.figure(figsize = (8, 3))
    plt.suptitle(f'Distance = {distance(embeddings[idx1], embeddings[idx2]):.2f}')
    plt.subplot(121)
    plt.imshow(load_image(metadata[idx1].image_path()))
    plt.subplot(122)
    plt.imshow(load_image(metadata[idx2].image_path()))

import matplotlib.pyplot as plt
show_pair(2, 3)
show_pair(2, 180)

show_pair(30, 31)
show_pair(30, 100)

show_pair(70, 72)
show_pair(70, 115)

train_idx = np.arange(metadata.shape[0]) % 9 != 0
test_idx = np.arange(metadata.shape[0]) % 9 == 0

# Features
X_train = np.array(embeddings)[train_idx]
X_test = np.array(embeddings)[test_idx]

# Labels
y_train = np.array([meta.name for meta in metadata[train_idx]])
y_test = np.array([meta.name for meta in metadata[test_idx]])

display(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

(9573, 2622)

(1197, 2622)

(9573,)

(1197,)

# Encode labels
en = LabelEncoder()
y_train = en.fit_transform(y_train)
y_test = en.transform(y_test)

# Standarize features
sc = StandardScaler()
X_train_sc = sc.fit_transform(X_train)
X_test_sc = sc.transform(X_test)

6. Use PCA for dimensionality reduction. [2 Marks]

Reduce feature dimensions using Principal Component Analysis

# Covariance matrix
cov_matrix = np.cov(X_train_sc.T)

# Eigen values and vector
eig_vals, eig_vecs = np.linalg.eig(cov_matrix)

# Cumulative variance explained
tot = sum(eig_vals)
var_exp = [(i /tot) * 100 for i in sorted(eig_vals, reverse = True)]
cum_var_exp = np.cumsum(var_exp)

print('Cumulative Variance Explained', cum_var_exp)

Cumulative Variance Explained [ 13.56311351  18.97109499  22.94722615 ...  99.99999983  99.99999999
 100.        ]

# Get index where cumulative variance explained is > threshold
thres = 95
res = list(filter(lambda i: i > thres, cum_var_exp))[0]
index = (cum_var_exp.tolist().index(res))
print(f'Index of element just greater than {thres}: {str(index)}')

Index of element just greater than 95: 347

# Ploting
plt.figure(figsize = (15 , 7.2))
plt.bar(range(1, eig_vals.size + 1), var_exp, alpha = 0.5, align = 'center', label = 'Individual explained variance')
plt.step(range(1, eig_vals.size + 1), cum_var_exp, where = 'mid', label = 'Cumulative explained variance')
plt.axhline(y = thres, color = 'r', linestyle = '--')
plt.axvline(x = index, color = 'r', linestyle = '--')
plt.ylabel('Explained Variance Ratio')
plt.xlabel('Principal Components')
plt.legend(loc = 'best')
plt.tight_layout()
plt.show()

# Reducing the dimensions
pca = PCA(n_components = index, random_state = random_state, svd_solver = 'full', whiten = True)
pca.fit(X_train_sc)
X_train_pca = pca.transform(X_train_sc)
X_test_pca = pca.transform(X_test_sc)
display(X_train_pca.shape, X_test_pca.shape)

(9573, 347)

(1197, 347)

7. Build an SVM classifier in order to map each image to its right person. [4 Marks]

svc_pca = SVC(C = 1, gamma = 0.001, kernel = 'rbf', class_weight = 'balanced', random_state = random_state)
svc_pca.fit(X_train_pca, y_train)
print('SVC accuracy for train set: {0:.3f}'.format(svc_pca.score(X_train_pca, y_train)))

SVC accuracy for train set: 0.995

# Predict
y_pred = svc_pca.predict(X_test_pca)

# Accuracy Score
print('Accuracy Score: {}'.format(accuracy_score(y_test, y_pred).round(3)))

Accuracy Score: 0.967

names = [name.split('_')[1].title().strip() for name in labels]

# Classification Report
print('Classification Report: \n{}'.format(classification_report(y_test, y_pred, target_names = np.unique(names))))

Classification Report: 
                          precision    recall  f1-score   support

              Aaron Paul       1.00      1.00      1.00        10
      Alexandra Daddario       1.00      1.00      1.00        10
            Alvaro Morte       1.00      0.93      0.96        14
Alycia Debnam Carey Face       1.00      0.92      0.96        12
             Amanda Crew       1.00      1.00      1.00         8
          Amaury Nolasco       1.00      0.89      0.94         9
        Amber Heard Face       1.00      1.00      1.00         7
               Anna Gunn       1.00      0.93      0.96        14
           Anne Hathaway       1.00      0.92      0.96        13
     Barbara Palvin Face       1.00      0.89      0.94         9
      Bellamy Blake Face       1.00      0.92      0.96        13
    Benedict Cumberbatch       1.00      1.00      1.00        12
            Betsy Brandt       1.00      1.00      1.00        10
              Bill Gates       0.91      1.00      0.95        10
        Brenton Thwaites       1.00      1.00      1.00        16
             Brie Larson       1.00      1.00      1.00        15
            Brit Marling       1.00      0.92      0.96        13
          Bryan Cranston       1.00      1.00      1.00        14
              Caity Lotz       1.00      0.92      0.96        12
        Cameron Monaghan       1.00      1.00      1.00        13
   Chadwick Boseman Face       1.00      1.00      1.00        17
          Chance Perdomo       0.75      1.00      0.86         9
             Chris Evans       1.00      0.93      0.96        14
             Chris Pratt       0.93      1.00      0.96        13
          Cobie Smulders       1.00      0.87      0.93        15
      Danielle Panabaker       1.00      1.00      1.00        15
             Dave Franco       0.93      1.00      0.96        13
            David Mazouz       1.00      1.00      1.00        11
         Dominic Purcell       1.00      1.00      1.00        12
                   Drake       1.00      1.00      1.00         6
           Dua Lipa Face       1.00      1.00      1.00        11
          Dwayne Johnson       1.00      1.00      1.00        12
            Eliza Taylor       1.00      0.85      0.92        13
    Elizabeth Olsen Face       1.00      1.00      1.00        10
               Elon Musk       1.00      1.00      1.00        17
           Emilia Clarke       1.00      0.92      0.96        13
     Emily Bett Rickards       1.00      0.80      0.89         5
              Emma Stone       1.00      1.00      1.00        14
        Emma Watson Face       1.00      1.00      1.00         8
          Gal Gadot Face       1.00      1.00      1.00        14
       Grant Gustin Face       1.00      1.00      1.00        11
         Gwyneth Paltrow       1.00      1.00      1.00        13
             Henry Cavil       1.00      1.00      1.00         9
            Jason Isaacs       1.00      1.00      1.00        14
             Jason Momoa       0.56      1.00      0.72         9
              Jeff Bezos       1.00      1.00      1.00        11
           Jeremy Renner       1.00      1.00      1.00         7
         Jesse Eisenberg       1.00      0.88      0.93         8
             Jim Parsons       1.00      0.90      0.95        10
            Jon Bernthal       1.00      1.00      1.00        11
             Josh Radnor       1.00      1.00      1.00        11
          Kiernan Shipka       1.00      1.00      1.00        13
           Kit Harington       1.00      0.88      0.93        16
    Kristen Stewart Face       0.85      1.00      0.92        11
          Krysten Ritter       1.00      1.00      1.00        14
         Kumail Nanjiani       1.00      1.00      1.00        11
     Lindsey Morgan Face       1.00      1.00      1.00        10
         Maisie Williams       1.00      1.00      1.00         9
      Margot Robbie Face       0.91      0.91      0.91        11
           Maria Pedraza       1.00      1.00      1.00         9
            Mark Ruffalo       0.93      1.00      0.96        13
         Mark Zuckerberg       0.94      0.88      0.91        17
            Martin Starr       1.00      1.00      1.00        15
          Melissa Benoit       1.00      0.94      0.97        18
           Miguel Herran       0.93      0.88      0.90        16
             Mike Colter       1.00      1.00      1.00        17
      Millie Bobby Brown       0.94      1.00      0.97        17
         Morena Baccarin       0.88      0.94      0.91        16
          Morgan Freeman       0.83      1.00      0.91        10
         Natalie Portman       1.00      1.00      1.00        10
     Neil Patrick Harris       1.00      0.87      0.93        15
               Paul Rudd       1.00      1.00      1.00        14
            Pedro Alonso       1.00      1.00      1.00        12
          Peter Dinklage       1.00      1.00      1.00         4
              Rami Melek       0.94      1.00      0.97        15
                 Rihanna       0.75      1.00      0.86        12
                Rj Mitte       1.00      1.00      1.00        20
   Robert Downey Jr Face       1.00      0.89      0.94         9
          Robert Knepper       0.94      0.94      0.94        18
            Robin Taylor       0.86      1.00      0.92        18
           Ryan Reynolds       1.00      1.00      1.00        13
     Sarah Wayne Callies       1.00      1.00      1.00        13
      Scarlett Johansson       1.00      1.00      1.00        10
            Sean Pertwee       0.95      0.95      0.95        19
          Sebastian Stan       1.00      0.85      0.92        13
            Selena Gomez       0.88      0.88      0.88         8
                 Shakira       0.94      1.00      0.97        15
           Sophie Turner       0.75      0.86      0.80         7
           Stephen Amell       1.00      1.00      1.00         7
           Sundar Pichai       1.00      1.00      1.00         9
          Tati Gabrielle       1.00      1.00      1.00         9
            Taylor Swift       1.00      1.00      1.00        13
      Thomas Middleditch       0.92      0.92      0.92        12
            Tom Cavanagh       1.00      0.80      0.89        10
        Tom Holland Face       0.91      1.00      0.95        10
          Ursula Corbero       1.00      1.00      1.00         5
        Wentworth Miller       1.00      1.00      1.00         7
           Willa Holland       1.00      1.00      1.00        11
        William Fichtner       1.00      0.92      0.96        13
                 Zendaya       1.00      1.00      1.00        13

                accuracy                           0.97      1197
               macro avg       0.97      0.97      0.97      1197
            weighted avg       0.97      0.97      0.97      1197

8. Import and display the the test images. [2 Marks]

Hint: ‘Benedict Cumberbatch9.jpg’ and ‘Dwayne Johnson4.jpg’ are the test images

def sample_img_plot(sample_idx):
  # Load image for sample_idx from test data
  sample_img = load_image(metadata[test_idx][sample_idx].image_path())
  # Get actual name
  actual_name = metadata[test_idx][sample_idx].name.split('_')[-1].title().strip()
  # Normalizing pixel values
  sample_img = (sample_img/255.).astype(np.float32)
  # Resize
  sample_img = cv2.resize(sample_img, (224, 224))

  # Obtain embedding vector for sample image
  embedding = vgg_face_descriptor.predict(np.expand_dims(sample_img, axis = 0))[0]
  # Scaled the vector and reshape
  embedding_scaled = sc.transform(embedding.reshape(1, -1))
  # Predict
  sample_pred = svc_pca.predict(pca.transform(embedding_scaled))
  # Transform back
  pred_name = en.inverse_transform(sample_pred)[0].split('_')[-1].title().strip()
  return sample_img, actual_name, pred_name

# Plot for 10th image in test data
sample_img, actual_name, pred_name = sample_img_plot(10)
fig = plt.figure(figsize = (15, 7.2))
plt.axis('off')
plt.imshow(sample_img)
plt.title(f"A: {actual_name} \n P: {pred_name}", color = 'green' if actual_name == pred_name else 'red')
plt.show()

1/1 [==============================] - 1s 1s/step

9. Use the trained SVM model to predict the face on both test images. [4 Marks]

# Random 20 sample images from test data
plt.figure(figsize = (15, 15))
gs1 = gridspec.GridSpec(5, 4)
gs1.update(wspace = 0, hspace = 0.3)

for i in range(20):
    ax1 = plt.subplot(gs1[i])
    plt.axis('on')
    ax1.set_xticklabels([])
    ax1.set_yticklabels([])
    ax1.set_aspect('equal')

    sample_img, actual_name, pred_name = sample_img_plot(random.randint(1, 1197))

    plt.axis('off')
    plt.imshow(sample_img)

    plt.title(f"A: {actual_name} \n P: {pred_name}", color = 'green' if actual_name == pred_name else 'red')
plt.show()

1/1 [==============================] - 1s 572ms/step
1/1 [==============================] - 1s 600ms/step
1/1 [==============================] - 1s 643ms/step
1/1 [==============================] - 1s 1s/step
1/1 [==============================] - 1s 949ms/step
1/1 [==============================] - 1s 912ms/step
1/1 [==============================] - 1s 897ms/step
1/1 [==============================] - 1s 678ms/step
1/1 [==============================] - 1s 570ms/step
1/1 [==============================] - 1s 521ms/step
1/1 [==============================] - 1s 549ms/step
1/1 [==============================] - 1s 566ms/step
1/1 [==============================] - 1s 552ms/step
1/1 [==============================] - 1s 557ms/step
1/1 [==============================] - 1s 556ms/step
1/1 [==============================] - 1s 514ms/step
1/1 [==============================] - 1s 531ms/step
1/1 [==============================] - 1s 543ms/step
1/1 [==============================] - 1s 580ms/step
1/1 [==============================] - 1s 574ms/step

Conclusion

Task here was to recognize (aligned) faces from a dataset containing 10k+ images for 100 people using a pre-trained model on Face Recognition.

VGG model with pre-trained weights was used to generate embeddings for each images in the dataset.

Distance between two pair of images were also calculated and plotted.

Since, there were 2,622 features for each image, PCA was used for dimension reduction after standardizing the features.

With an cumulative explained variance of 95%, 347 PCA components were used.

Using SVC we predicted the labels for test dataset with an accuracy of more than 96%.

Also compared predicted and actual labels for a given sample image as well as for 20 random images from test dataset.